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Estimating the Effect of Cropland to Prairie Conversion on Peak Storm Run-Off

Authors

  • Philip J. Gerla

    Corresponding author
    1. The Nature Conservancy, 1101 West River Parkway, Suite 200, Minneapolis, MN 55415, U.S.A.
    2. Department of Geology and Geological Engineering, University of North Dakota, 81 Cornell Stop 8358, Grand Forks, ND 58202-8358, U.S.A.
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Address correspondence to P. J. Gerla, email pgerla@tnc.org

Abstract

Much of the original U.S. grassland has undergone conversion to cropland. During the last few years, large first- and second-order watershed scale projects have begun to reconstruct the native tallgrass prairie cover and biodiversity. The effect on watershed hydrological budget is largely unknown, especially concerning storm run-off. Curve number variability is used to estimate the uncertainty of peak run-off following the change in cover, given rainfall recurrence and watershed size. The method involves three steps: (1) estimate the time-of-concentration for many similar sized watersheds in the region, (2) define the probability distribution for time-of-concentration, curve numbers, and watershed area, (3) with these data, generate input variables for a Monte Carlo analysis, which can then be used to predict the mean and confidence interval of peak run-off. As an example, spatial and hydrological characteristics of first- and second-order watersheds ranging from 2 to 50 km2 in the Red River of the North basin provide a log normal probability distribution for time-of-concentration. Using the range of watershed area and a β probability distribution for curve number uncertainty, the analysis predicts the change in peak run-off from an ensemble of watershed realizations that characterize cropland to grassland conversion. Results suggest that given five- and 25-year, 24-hour rainfall recurrence, average reduction in peak run-off will range from 50 to 55% and 40 to 45%, respectively, for the basin. A large range of uncertainty at the 80% confidence interval, however, indicates that an accurate prediction requires analysis for specific watersheds.

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